CAS-Climate: Understanding the Changing Climatology, Organizing Patterns and Source Attribution of Hazards of Floods over the Southcentral and Southeast US

CAS-气候：了解美国中南部和东南部洪水灾害的气候变化、组织模式和来源归因

• 批准号: 2208562
• 负责人: Sankarasubraman Arumugam
• 金额: $ 67.34万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2208562&HistoricalAwards=false
• 关键词: CAS; Climate; Understanding; Changing; Climatology

Floods often lead to significant loss of life and property. This project will focus on understanding how watershed conditions and atmospheric and oceanic conditions cause monthly flooding over the Southcentral and Southeast US region (SESC) as the region is vulnerable to flooding throughout the year. Given the increasing strength and frequency of tropical storms (2020 set a record with 30 named storms in the Atlantic and 12 making landfall in the SESC), this project will enhance the scientific understanding of flood hazards and will also better inform the wider community of forecasters and decision makers. For instance, five recent major hurricanes – Matthew (2016), Harvey (2017), Irma (2017), Florence (2018), and Ida (2021) – led to catastrophic flooding over the SESC causing major challenges in preparedness and recovery for local, state and federal agencies. The project team will engage with climate offices, National Environmental Modeling and Analysis Center (NEMAC) at UNC, Asheville and National Centers for Environmental Information (NCEI). The project team will also collaborate with faculty and graduate students from minority-serving institutions as part of the summer internship program at NC State University. Results from the project will be disseminated through peer reviewed publications, seminars and workshops.The objective of this proposal is to improve understanding of monthly flood dynamics over the SESC US by (a) quantifying the shift in climatology, (b) identifying their moisture delivery pathways, and (c) attributing the sources (land surface, atmosphere and ocean) that modulate their spatiotemporal variability. The project team will examine the shift in climatology and interannual variability of monthly daily/3-day maximum streamflow in Hydroclimatic Data Network (HCDN) basins over the SESC. The principal investigators will also use a variety of observed and reanalysis data, climatic indices, as well as statistical and physical (numerical weather prediction) models. The shift in climatology will be quantified using optimal climate normal and “hinge fit” methodologies, and the organizational and moisture delivery patterns will be analyzed using multiple Lagrangian particle tracking models. Attribution of sources related to monthly flood climatology will be quantified using rigorous statistical techniques (e.g., random forest, hinge fit) and through physical modeling. A Bayesian Hierarchical Model (BHM) will combine the contribution from five identified sources – 1) shift in climatology and sources of moisture transport, 2) teleconnections, 3) atmospheric rivers (ARs), 4) tropical cyclones (TCs), and 5) initial land-surface conditions – that influence flood processes over multiple spatial scales for explaining the observed monthly flood variability in the SESC region.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

洪灾往往造成重大的生命和财产损失。该项目将侧重于了解分水岭条件以及大气和海洋条件如何导致美国中南部和东南部地区(SESC)的月度洪灾，因为该地区全年都容易发生洪灾。鉴于热带风暴的强度和频率不断增加(2020年创下记录，有30个命名风暴在大西洋，12个在SESC登陆)，该项目将加强对洪水灾害的科学了解，也将更好地向更广泛的预报员和决策者提供信息。例如，最近的五次主要飓风--马修(2016)、哈维(2017)、伊尔玛(2017)、佛罗伦萨(2018)和伊达(2021)--导致SESC发生灾难性洪灾，给地方、州和联邦机构的备灾和恢复工作带来重大挑战。项目小组将与气候办公室、北卡罗来纳大学的国家环境建模和分析中心(NEMAC)、阿什维尔和国家环境信息中心(NCEI)进行接触。该项目团队还将与少数族裔服务机构的教职员工和研究生合作，作为北卡罗来纳州立大学暑期实习计划的一部分。该项目的结果将通过同行评议的出版物、研讨会和研讨会传播。这项建议的目的是通过(A)量化气候学的变化，(B)确定其水分输送途径，以及(C)确定影响其时空变异性的来源(陆地表面、大气和海洋)，从而提高对SESC US每月洪水动态的理解。项目组将研究SESC上水文气候数据网络(HCDN)流域每月每日/3日最大径流的气候学变化和年际变化。首席调查员还将使用各种观测和再分析数据、气候指数以及统计和物理(数值天气预报)模型。气候学的转变将使用最佳气候正常和“铰链匹配”方法进行量化，组织和水分输送模式将使用多个拉格朗日粒子跟踪模型进行分析。将使用严格的统计技术(例如随机森林、铰链拟合)和物理模型来量化与每月洪水气候学有关的来源的归属。贝叶斯分级模式(BHM)将结合五个确定的来源的贡献-1)气候学和水汽输送来源的变化，2)遥相关，3)大气河流(AR)，4)热带气旋(TCS)，和5)初始地表条件-在多个空间尺度上影响洪水过程，以解释在SESC地区观察到的每月洪水变异性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。